Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit
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Updated
May 23, 2024 - Python
Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit
This project uses a variety of advanced voiceprint recognition models such as EcapaTdnn, ResNetSE, ERes2Net, CAM++, etc. It is not excluded that more models will be supported in the future. At the same time, this project also supports MelSpectrogram, Spectrogram data preprocessing methods
本项目使用了EcapaTdnn、ResNetSE、ERes2Net、CAM++等多种先进的声纹识别模型,同时本项目也支持了MelSpectrogram、Spectrogram、MFCC、Fbank等多种数据预处理方法
The Pytorch implementation of sound classification supports EcapaTdnn, PANNS, TDNN, Res2Net, ResNetSE and other models, as well as a variety of preprocessing methods.
基于PaddlePaddle实现的音频分类,支持EcapaTdnn、PANNS、TDNN、Res2Net、ResNetSE等各种模型,还有多种预处理方法
Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)
Speaker verification of virtual assistants using ECAPA-TDNN model from SpeechBrain toolkit and transfer learning approach emphasizing on inter and intra comparision (text independent and dependent).
Verifying the identity of a person from characteristics of the voice independent from language via NVIDIA NeMo models (ECAPA-TDNN, SpeakerNet, TitaNet-L).
CryCeleb2023 experiments
针对CN-Celeb数据集的基于ECAPA-TDNN的说话人识别的pytorch实现
This repository contain the code of the main part of my master thesis degree at Politecnico di Torino in Data science & Engineering
Speaker verification task with ECAPA-TDNN model (trained on Persian dataset)
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